The cost of coordination can exceed the benefit of collaboration in
performing complex tasks
- URL: http://arxiv.org/abs/2009.11038v3
- Date: Fri, 27 Jan 2023 19:04:22 GMT
- Title: The cost of coordination can exceed the benefit of collaboration in
performing complex tasks
- Authors: Vince J. Straub and Milena Tsvetkova and Taha Yasseri
- Abstract summary: dyads gradually improve in performance but do not experience a collective benefit compared to individuals in most situations.
Having an additional expert in the dyad who is adequately trained improves accuracy.
Findings highlight that the extent of training received by an individual, the complexity of the task at hand, and the desired performance indicator are all critical factors that need to be accounted for when weighing up the benefits of collective decision-making.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Humans and other intelligent agents often rely on collective decision making
based on an intuition that groups outperform individuals. However, at present,
we lack a complete theoretical understanding of when groups perform better.
Here we examine performance in collective decision-making in the context of a
real-world citizen science task environment in which individuals with
manipulated differences in task-relevant training collaborated. We find 1)
dyads gradually improve in performance but do not experience a collective
benefit compared to individuals in most situations; 2) the cost of coordination
to efficiency and speed that results when switching to a dyadic context after
training individually is consistently larger than the leverage of having a
partner, even if they are expertly trained in that task; and 3) on the most
complex tasks having an additional expert in the dyad who is adequately trained
improves accuracy. These findings highlight that the extent of training
received by an individual, the complexity of the task at hand, and the desired
performance indicator are all critical factors that need to be accounted for
when weighing up the benefits of collective decision-making.
Related papers
- Code Collaborate: Dissecting Team Dynamics in First-Semester Programming Students [3.0294711465150006]
The study highlights the collaboration trends that emerge as first-semester students develop a 2D game project.
Results indicate that students often slightly overestimate their contributions, with more engaged individuals more likely to acknowledge mistakes.
Team performance shows no significant variation based on nationality or gender composition, though teams that disbanded frequently consisted of lone wolves.
arXiv Detail & Related papers (2024-10-28T11:42:05Z) - Multi-agent cooperation through learning-aware policy gradients [53.63948041506278]
Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning.
We present the first unbiased, higher-derivative-free policy gradient algorithm for learning-aware reinforcement learning.
We derive from the iterated prisoner's dilemma a novel explanation for how and when cooperation arises among self-interested learning-aware agents.
arXiv Detail & Related papers (2024-10-24T10:48:42Z) - Adaptive Control in Assistive Application -- A Study Evaluating Shared Control by Users with Limited Upper Limb Mobility [4.858212893290674]
This study assesses an adaptive Degrees of Freedom control method specifically tailored for individuals with upper limb impairments.
It employs a between-subjects analysis with 24 participants, conducting 81 trials across three distinct input devices in a realistic everyday-task setting.
arXiv Detail & Related papers (2024-06-10T08:36:55Z) - Optimising Human-AI Collaboration by Learning Convincing Explanations [62.81395661556852]
We propose a method for a collaborative system that remains safe by having a human making decisions.
Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations.
arXiv Detail & Related papers (2023-11-13T16:00:16Z) - Flexible social inference facilitates targeted social learning when
rewards are not observable [58.762004496858836]
Groups coordinate more effectively when individuals are able to learn from others' successes.
We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior.
arXiv Detail & Related papers (2022-12-01T21:04:03Z) - Enhancing team performance with transfer-learning during real-world
human-robot collaboration [0.0]
Transfer learning was integrated in a deep Reinforcement Learning (dRL) agent.
Probability reuse method was used for the transfer learning (TL)
TL also affected the subjective performance of the teams and enhanced the perceived fluency.
arXiv Detail & Related papers (2022-11-23T16:02:00Z) - Human-Algorithm Collaboration: Achieving Complementarity and Avoiding
Unfairness [92.26039686430204]
We show that even in carefully-designed systems, complementary performance can be elusive.
First, we provide a theoretical framework for modeling simple human-algorithm systems.
Next, we use this model to prove conditions where complementarity is impossible.
arXiv Detail & Related papers (2022-02-17T18:44:41Z) - Towards Collaborative Question Answering: A Preliminary Study [63.91687114660126]
We propose CollabQA, a novel QA task in which several expert agents coordinated by a moderator work together to answer questions that cannot be answered with any single agent alone.
We make a synthetic dataset of a large knowledge graph that can be distributed to experts.
We show that the problem can be challenging without introducing prior to the collaboration structure, unless experts are perfect and uniform.
arXiv Detail & Related papers (2022-01-24T14:27:00Z) - Improved cooperation by balancing exploration and exploitation in
intertemporal social dilemma tasks [2.541277269153809]
We propose a new learning strategy for achieving coordination by incorporating a learning rate that can balance exploration and exploitation.
We show that agents that use the simple strategy improve a relatively collective return in a decision task called the intertemporal social dilemma.
We also explore the effects of the diversity of learning rates on the population of reinforcement learning agents and show that agents trained in heterogeneous populations develop particularly coordinated policies.
arXiv Detail & Related papers (2021-10-19T08:40:56Z) - Efficiently Identifying Task Groupings for Multi-Task Learning [55.80489920205404]
Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
We suggest an approach to select which tasks should train together in multi-task learning models.
Our method determines task groupings in a single training run by co-training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss.
arXiv Detail & Related papers (2021-09-10T02:01:43Z) - Human-Robot Team Coordination with Dynamic and Latent Human Task
Proficiencies: Scheduling with Learning Curves [0.0]
We introduce a novel resource coordination that enables robots to explore the relative strengths and learning abilities of their human teammates.
We generate and evaluate a robust schedule while discovering the latest individual worker proficiency.
Results indicate that scheduling strategies favoring exploration tend to be beneficial for human-robot collaboration.
arXiv Detail & Related papers (2020-07-03T19:44:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.